US 12,235,884 B2
Facilitating reduction of noise in non-standard printed circuit board assembly component descriptions using a zero-shot model to identify salient component class descriptions
Ravi Shukla, KA (IN); Jeffrey Vah, Round Rock, TX (US); Jerrold Cady, Georgetown, TX (US); Bharathi Raja Kalyanasundaram, Tamil Nadu (IN); and Aaron Sanchez, Austin, TX (US)
Assigned to DELL PRODUCTS L.P., Round Rock, TX (US)
Filed by Dell Products L.P., Round Rock, TX (US)
Filed on Oct. 27, 2022, as Appl. No. 17/975,484.
Prior Publication US 2024/0143639 A1, May 2, 2024
Int. Cl. G06F 16/34 (2019.01); G06F 40/30 (2020.01); G06F 40/40 (2020.01); G06N 5/022 (2023.01); G06N 20/00 (2019.01)
CPC G06F 16/345 (2019.01) [G06F 40/30 (2020.01); G06F 40/40 (2020.01); G06N 5/022 (2013.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising:
at least one processor; and
at least one memory that stores executable components that, when executed by the at least one processor, facilitate performance of operations by the system, the operations comprising:
receiving a group of defined valid label designations representing an accepted domain of component class descriptions;
receiving a group of defined invalid label designations representing a rejected domain of component class descriptions; and
for each component description of a group of component descriptions,
replacing non-alphanumeric characters of the component description with respective spaces to obtain a revised component description,
removing, from the revised component description, words that comprise numbers to obtain a reduced component description,
expanding, using a defined knowledge base comprising an online library of information, respective words of the reduced component description to obtain respective expanded words representing natural-language expressions of the respective words of the reduced component description,
based on the group of defined valid label designations, the group of defined invalid label designations, and the respective expanded words, selecting, using a zero-shot model comprising a pre-trained machine learning model, a group of words from the reduced component description to be included in a final reduced component description representing the accepted domain of component class descriptions, and
based on a defined error condition and using a group of machine learning models, selecting the final reduced component description as a candidate of a component failure.